Abstract

Background

A single-step blending approach allows genomic prediction using information of genotyped
and non-genotyped animals simultaneously. However, the combined relationship matrix
in a single-step method may need to be adjusted because marker-based and pedigree-based
relationship matrices may not be on the same scale. The same may apply when a GBLUP
model includes both genomic breeding values and residual polygenic effects. The objective
of this study was to compare single-step blending methods and GBLUP methods with and
without adjustment of the genomic relationship matrix for genomic prediction of 16
traits in the Nordic Holstein population.

Methods

The data consisted of de-regressed proofs (DRP) for 5 214 genotyped and 9 374 non-genotyped
bulls. The bulls were divided into a training and a validation population by birth
date, October 1, 2001. Five approaches for genomic prediction were used: 1) a simple
GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method
with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step
blending method. In the adjusted GBLUP and single-step methods, the genomic relationship
matrix was adjusted for the difference of scale between the genomic and the pedigree
relationship matrices. A set of weights on the pedigree relationship matrix (ranging
from 0.05 to 0.40) was used to build the combined relationship matrix in the single-step
blending method and the GBLUP method with a polygenetic effect.

Results

Averaged over the 16 traits, reliabilities of genomic breeding values predicted using
the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3% higher
than reliabilities from the simple GBLUP method (without a polygenic effect). The
adjusted single-step blending and original single-step blending methods (relative
weight of 0.20) had average reliabilities that were 2.1% and 1.8% higher than the
simple GBLUP method, respectively. In addition, the GBLUP method with a polygenic
effect led to less bias of genomic predictions than the simple GBLUP method, and both
single-step blending methods yielded less bias of predictions than all GBLUP methods.

Conclusions

The single-step blending method is an appealing approach for practical genomic prediction
in dairy cattle. Genomic prediction from the single-step blending method can be improved
by adjusting the scale of the genomic relationship matrix.